code: def streamline_signal(streamline, gtab, evals=(0.001, 0, 0)):
      """
      The signal from a single streamline estimate along each of its nodes.
  
      Parameters
      ----------
      streamline : a single streamline
  
      gtab : GradientTable class instance
  
      evals : array-like of length 3, optional
          The eigenvalues of the canonical tensor used as an estimate of the
          signal generated by each node of the streamline.
      """
      # Gotta have those tensors:
      tensors = streamline_tensors(streamline, evals)
      sig = np.empty((len(streamline), np.sum(~gtab.b0s_mask)))
      # Extract them once:
      bvecs = gtab.bvecs[~gtab.b0s_mask]
      bvals = gtab.bvals[~gtab.b0s_mask]
      for ii, tensor in enumerate(tensors):
          ADC = np.diag(np.linalg.multi_dot([bvecs, tensor, bvecs.T]))
          # Use the Stejskal-Tanner equation with the ADC as input, and S0 = 1:
          sig[ii] = np.exp(-bvals * ADC)
      return sig - np.mean(sig)
  
imports: import numpy
  import scipy.sparse
  import scipy.linalg
  from dipy.reconst.base import ReconstModel, ReconstFit
  from dipy.tracking.utils import unique_rows
  from dipy.tracking.streamline import transform_streamlines
  from dipy.tracking.vox2track import _voxel2streamline
  import dipy.data
  import dipy.core.optimize
label_desc: Use image node_id 5 for calling a global function with example usage: streamline_signal(streamline, gtab, evals) and returns: unknown